Biology subjects

Pino, E.

Publications and source records attributed to Pino, E..

6 recordsLinked to original sources

bioRxiv2026

A supervised digital game intervention supports language and communication in young children.

The role of digital media in early childhood development remains highly debated, particularly regarding its impact on language acquisition. While excessive or unsupervised screen exposure has been linked to poorer outcomes, less is known about whether structured and interactive uses of technology can support learning. Building on previous research, we evaluated a brief, educator-supervised tablet-based intervention in 246 children aged 2-5 years from low- to middle-socioeconomic backgrounds attending public early education centers. Using a pre-post design with matched study and control groups, children completed 4-8 short training sessions (15 minutes each) involving interactive word-image associations spanning multiple linguistic categories. Preschoolers additionally engaged in prompted vocalization. Across age groups (2-3, 3-4, and 4-5 years), children in the intervention showed greater gains in language comprehension than controls, including receptive language in toddlers ({beta} = 0.49, p = 0.009), vocabulary and morphology in younger preschoolers ({beta} = 0.59-0.68, all p < 0.05), and grammar comprehension in older preschoolers ({beta} = 0.30, p = 0.038). These effects were consistent after accounting for child and parental characteristics. Together, these findings suggest that the developmental impact of digital media depends less on exposure itself than on how it is used. When embedded in structured, socially guided interactions, even brief tablet-based activities may support early language development

developmental biology↗

bioRxiv2025

PRESYNAPTIC MUSCARINIC-NICOTINIC MODULATION of GABAergic INPUTS to NEURONS in the REM SLEEP (REM-S) EXECUTIVE AREA of the ROSTRAL PONS

A large body of evidence indicates that cholinergic neurons in the brainstem tegmental nuclei play a pivotal role in controlling neurons of the reticular nucleus pontis oralis (PnO), an area considered executive for rapid eye movement (REM) sleep onset and maintenance. More recent data indicate that the PnO is also under GABAergic control -which gates REM sleep- thereby highlighting the importance of interactions between cholinergic and GABAergic processes as a key mechanism in REM sleep regulation. Here we employed a rat mesopontine slice preparation to investigate, in vitro, the modulation of GABAergic inputs to PnO neurons by cholinergic agents. Carbachol, a mixed muscarinic-nicotinic cholinergic agonist, provoked either depression or facilitation of single monosynaptic GABAergic IPSCs evoked by extracellular stimulation of the region of brainstem tegmental nuclei. Both effects were presynaptic in origin and are likely attributable to the activation of distinct presynaptic cholinergic receptors as depression was replicated by muscarine (presynaptic inhibition) and facilitation by nicotine (presynaptic facilitation). Furthermore, IPSCs evoked by stimulation patterns mimicking physiological activity (short trains at 15 Hz) were also affected by cholinergic agonists. Muscarine caused presynaptic inhibition accompanied by frequency facilitation and nicotine promoted the opposite effect. Notably, both agonists reduced the total inhibitory charge transferred to postsynaptic neurons during the stimulus train. These findings suggest an additional mechanism by which cholinergic modulation can relieve GABAergic inhibition of PnO neurons under physiological conditions, serving as a local component of the reciprocal inhibitory interactions between sleep-regulating networks in REM sleep brainstem executive areas. NEW & NOTEWORTHYDespite decades of research into brainstem sleep mechanisms, the GABAergic-cholinergic interactions involved in gating and ultradian regulation of REM sleep remain poorly understood. We provide evidence for the functional expression of presynaptic muscarinic and nicotinic cholinergic receptors at GABAergic terminals within the pontine reticular nucleus (PnO), a region central to REM sleep onset and maintenance. Our results indicate that cholinergic signaling might relieve GABAergic inhibition of PnO neurons. As a component of a mutual inhibitory mechanism between REM sleep controlling systems, this interaction may sharpen transitions into REM sleep.

neuroscience↗

bioRxiv2025

Synergistic olfactory nerve input and cholinergic neuromodulation can activate ERK in rat olfactory bulb vasopressin cells

Social discrimination in rats relies on vasopressin cells (VPCs) intrinsic to the olfactory bulb (OB). We had observed that VPCs responded to electrical stimulation of the olfactory nerve in acute OB slices with inhibitory postsynaptic potentials, and that the neuromodulator acetylcholine could revert these responses to excitation, resulting in action potentials (AP). Moreover, in behaving rats that were exposed to conspecifics, more VPCs were immunopositive to the neural activity marker pERK (pERK+ VPC) than in control rats. However, it is unclear whether these two observations, the increased ERK activation in vivo, and the generation of APs in the presence of ACh in vitro, can be actually mapped onto each other. Here we investigated ERK activation in acute OB slices from transgenic VP-eGFP rats upon either chemical stimulation or tetanic olfactory nerve stimulation. Both KCl and NMDA stimulation resulted in substantial pERK induction across bulbar layers and caused VPC spiking in whole-cell recordings, but only NMDA slightly increased pERK+ VPC percentage. Tetanic olfactory nerve stimulation yielded localized, column-like ERK activation of neurons across bulbar layers. The presence of ACh during tetanic stimulation substantially and specifically increased percentages of pERK+ VPCs within columns, indicating that columnar pERK+ VPCs were preferentially activated by coincident cholinergic neuromodulation and synaptic input. Our results validate pERK induction as a tool to monitor synaptic VPC excitation in the OB, and imply that depolarization of VPCs alone is insufficient to activate ERK. We propose that synaptically evoked APs are a prerequisite for pERK induction in VPCs.

neuroscience↗

bioRxiv2024

Neural correlates in the time course of inferences: costs and benefits for less-skilled readers at university level.

Inferences are an indicator of a greater reading comprehension, as they imply a combination of implicit and explicit information that usually combines a textual representation with background knowledge of the reader. The aim of this study is to explore the costs and benefits of the time course of inferences in university students with reading comprehension difficulties at 3 stages during a narration. The method used was the event-related potential (ERP) technique in order to register the brain activity of 63 teaching program students while they read familiar, less-familiar and neutral stories. Results show a slow negativity potential component with greater negativity in words coming from familiar contexts when compared to less familiar and neutral ones in the first locus; an N400 component and a Post-N400 component in the second locus, reflecting greater negativity in familiar contexts when compared to less-familiar ones; and, lastly, through the use of a lexical decision task, FN400 and N400 components were found in the third locus, especially for pseudowords. These results are interpreted as a preferably bottom-up processing, which is characterized by lexical access difficulties in less-skilled readers.

neuroscience↗

bioRxiv2018

Effect of a Sepsis Prediction Algorithm on Patient Mortality, Length of Stay, and Readmission

ObjectiveTo validate performance of a machine learning algorithm for severe sepsis determination up to 48 hours before onset, and to evaluate the effect of the algorithm on in-hospital mortality, hospital length of stay, and 30-day readmission.\n\nSettingThis cohort study includes a combined retrospective analysis and clinical outcomes evaluation: a dataset containing 510,497 patient encounters from 461 United States health centers for retrospective analysis, and a multiyear, multicenter clinical data set of real-world data containing 75,147 patient encounters from nine hospitals for clinical outcomes evaluation.\n\nParticipantsFor retrospective analysis, 270,438 adult patients with at least one documented measurement of five out of six vital sign measurements were included. For clinical outcomes analysis, 17,758 adult patients who met two or more Systemic Inflammatory Response Syndrome (SIRS) criteria at any point during their stay were included.\n\nResultsAt severe sepsis onset, the MLA demonstrated an AUROC of 0.91 (95% CI 0.90, 0.92), which exceeded those of MEWS (0.71, P<001), SOFA (0.74; P<.001), and SIRS (0.62; P<.001). For severe sepsis prediction 48 hours in advance of onset, the MLA achieved an AUROC of 0.77 (95% CI 0.73, 0.80). For the clinical outcomes study, when using the MLA, hospitals saw an average 39.5% reduction of in-hospital mortality, a 32.3% reduction in hospital length of stay, and a 22.7% reduction in 30-day readmission rate.\n\nConclusionsThe MLA accurately predicts severe sepsis onset up to 48 hours in advance using only readily available vital signs in retrospective validation. Reductions of in-hospital mortality, hospital length of stay, and 30-day readmissions were observed in real-world clinical use of the MLA. Results suggest this system may improve severe sepsis detection and patient outcomes over the use of rules-based sepsis detection systems.\n\nKEY POINTSO_ST_ABSQuestionC_ST_ABSIs a machine learning algorithm capable of accurate severe sepsis prediction, and does its clinical implementation improve patient mortality rates, hospital length of stay, and 30-day readmission rates?\n\nFindingsIn a retrospective analysis that included datasets containing a total of 585,644 patient encounters from 461 hospitals, the machine learning algorithm demonstrated an AUROC of 0.93 at time of severe sepsis onset, which exceeded those of MEWS (0.71), SOFA (0.74), and SIRS (0.62); and an AUROC of 0.77 for severe sepsis prediction 48 hours in advance of onset. In an analysis of real-world data from nine hospitals across 75,147 patient encounters, use of the machine learning algorithm was associated with a 39.5% reduction in in-hospital mortality, a 32.3% reduction in hospital length of stay, and a 22.7% reduction in 30-day readmission rate.\n\nMeaningThe accurate and predictive nature of this algorithm may encourage early recognition of patients trending toward severe sepsis, and therefore improve sepsis related outcomes.\n\nSTRENGTHS AND LIMITATIONS OF THIS STUDYO_LIA retrospective study of machine learning severe sepsis prediction from a dataset with 510,497 patient encounters demonstrates high accuracy up to 48 hours prior to onset.\nC_LIO_LIA multicenter clinical study of real-world data using this machine learning algorithm for severe sepsis alerts achieved reductions of in-hospital mortality, length of stay, and 30-day readmissions.\nC_LIO_LIThe required presence of an ICD-9 code to classify a patient as severely septic in our retrospective analysis potentially limits our ability to accurately classify all patients.\nC_LIO_LIOnly adults in US hospitals were included in this study.\nC_LIO_LIFor the real-world section of the study, we cannot eliminate the possibility that implementation of a sepsis algorithm raised general awareness of sepsis within a hospital, which may lead to higher recognition of septic patients, independent of algorithm performance.\nC_LI

bioinformatics↗

bioRxiv2017

Evaluating a sepsis prediction machine learning algorithm using minimal electronic health record data in the emergency department and intensive care unit

IntroductionSepsis is a major health crisis in US hospitals, and several clinical identification systems have been designed to help care providers with early diagnosis of sepsis. However, many of these systems demonstrate low specificity or sensitivity, which limits their clinical utility. We evaluate the effects of a machine learning algodiagnostic (MLA) sepsis prediction and detection system using a before-and-after clinical study performed at Cabell Huntington Hospital (CHH) in Huntington, West Virginia. Prior to this study, CHH utilized the St. Johns Sepsis Agent (SJSA) as a rules-based sepsis detection system.\n\nMethodsThe Predictive algoRithm for EValuation and Intervention in SEpsis (PREVISE) study was carried out between July 1, 2017 and August 30, 2017. All patients over the age of 18 who were admitted to the emergency department or intensive care units at CHH were monitored during the study. We assessed pre-implementation baseline metrics during the month of July, 2017, when the SJSA was active. During implementation in the month of August, 2017, SJSA and the MLA concurrently monitored patients for sepsis risk. At the conclusion of the study period, the primary outcome of sepsis-related in-hospital mortality and secondary outcome of sepsis-related hospital length of stay were compared between the two groups.\n\nResultsSepsis-related in-hospital mortality decreased from 3.97% to 2.64%, a 33.5% relative decrease (P = 0.038), and sepsis-related length of stay decreased from 2.99 days in the pre-implementation phase to 2.48 days in the post-implementation phase, a 17.1% relative reduction (P < 0.001).\n\nConclusionReductions in patient mortality and length-of-stay were observed with use of a machine learning algorithm for early sepsis detection in the emergency department and intensive care units at Cabell Huntington Hospital, and may present a method for improving patient outcomes.\n\nTrial RegistrationClinicalTrials.gov, NCT03235193, retrospectively registered on July 27th 2017.

clinical trials↗